Aisu  Joesph

Aisu Joesph

1624327316

Securing Microsoft Active Directory

Clustering

K-means is one of the simplest unsupervised machine learning algorithms that solve the well-known data clustering problem. Clustering is one of the most common data analysis tasks used to get an intuition about data structure. It is defined as finding the subgroups in the data such that each data points in different clusters are very different. We are trying to find the homogeneous subgroups within the data. Each group’s data points are similarly based on similarity metrics like a Euclidean-based distance or correlation-based distance.

The algorithm can do clustering analysis based on features or samples. We try to find the subcategory of sampling based on attributes or try to find the subcategory of parts based on samples. The practical applications of such a procedure are many: the best use of clustering in amazon and Netflix recommended system, given a medical image of a group of cells, a clustering algorithm could aid in identifying the centers of the cells; looking at the GPS data of a user’s mobile device, their more frequently visited locations within a certain radius can be revealed; for any set of unlabeled observations, clustering helps establish the existence of some structure of data that might indicate that the data is separable.

What is K-Means Clustering?

K-means the clustering algorithm whose primary goal is to group similar elements or data points into a cluster.

K in k-means represents the number of clusters.

A cluster refers to a collection of data points aggregated together because of certain similarities.

K-means clustering is an iterative algorithm that starts with k random numbers used as mean values to define clusters. Data points belong to the group represented by the mean value to which they are closest. This mean value co-ordinates called the centroid.

Iteratively, the mean value of each cluster’s data points is computed, and the new mean values are used to restart the process till the mean stops changing. The disadvantage of k-means is that it a local search procedure and could miss global patterns.

The k initial centroids can be randomly selected. Another approach of determining k is to compute the entire dataset’s mean and add _k _random co-ordinates to it to make k initial points. Another method is to determine the principal component of the data and divide it into _k _equal partitions. The mean of each section can be used as initial centroids.

#ad #microsoft #microsoft-azure #azure #azure-functions #azure-security

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Securing Microsoft Active Directory
Wilford  Pagac

Wilford Pagac

1600448400

Office 365 Phishing Attack Leverages Real-Time Active Directory Validation

Researchers have uncovered a phishing attack using a new technique: Attackers are making use of authentication APIs to validate victims’ Office 365 credentials – in real time – as they enter them into the landing page.

Authentication APIs are used by apps and services running on the users’ behalf to access their data, Prashanth Arun, head of Data Science at Armorblox, told Threatpost. Office 365 requires app registrations to use APIs – but registrations require only an email address, making them seamless for attackers to leverage. Some additional configuration for the app also requires users to specify a website to “receive” authentication info, Arun added.

In a phishing attack recently spotted by researchers, the attacker used the authentication APIs to cross check the credentials of a senior executive at a large enterprise firm with the organization’s Azure Active directory. Active Directory (AD) is Microsoft’s proprietary directory service, which allows administrators to manage permissions and access to network resources. The authentication APIs use Azure AD to provide authentication services.

#hacks #web security #active directory #authentication api #credentials #email attack #microsoft #microsoft active directory #office 365 #phishing attack #phishing email

Wilford  Pagac

Wilford Pagac

1596789120

Best Custom Web & Mobile App Development Company

Everything around us has become smart, like smart infrastructures, smart cities, autonomous vehicles, to name a few. The innovation of smart devices makes it possible to achieve these heights in science and technology. But, data is vulnerable, there is a risk of attack by cybercriminals. To get started, let’s know about IoT devices.

What are IoT devices?

The Internet Of Things(IoT) is a system that interrelates computer devices like sensors, software, and actuators, digital machines, etc. They are linked together with particular objects that work through the internet and transfer data over devices without humans interference.

Famous examples are Amazon Alexa, Apple SIRI, Interconnected baby monitors, video doorbells, and smart thermostats.

How could your IoT devices be vulnerable?

When technologies grow and evolve, risks are also on the high stakes. Ransomware attacks are on the continuous increase; securing data has become the top priority.

When you think your smart home won’t fudge a thing against cybercriminals, you should also know that they are vulnerable. When cybercriminals access our smart voice speakers like Amazon Alexa or Apple Siri, it becomes easy for them to steal your data.

Cybersecurity report 2020 says popular hacking forums expose 770 million email addresses and 21 million unique passwords, 620 million accounts have been compromised from 16 hacked websites.

The attacks are likely to increase every year. To help you secure your data of IoT devices, here are some best tips you can implement.

Tips to secure your IoT devices

1. Change Default Router Name

Your router has the default name of make and model. When we stick with the manufacturer name, attackers can quickly identify our make and model. So give the router name different from your addresses, without giving away personal information.

2. Know your connected network and connected devices

If your devices are connected to the internet, these connections are vulnerable to cyber attacks when your devices don’t have the proper security. Almost every web interface is equipped with multiple devices, so it’s hard to track the device. But, it’s crucial to stay aware of them.

3. Change default usernames and passwords

When we use the default usernames and passwords, it is attackable. Because the cybercriminals possibly know the default passwords come with IoT devices. So use strong passwords to access our IoT devices.

4. Manage strong, Unique passwords for your IoT devices and accounts

Use strong or unique passwords that are easily assumed, such as ‘123456’ or ‘password1234’ to protect your accounts. Give strong and complex passwords formed by combinations of alphabets, numeric, and not easily bypassed symbols.

Also, change passwords for multiple accounts and change them regularly to avoid attacks. We can also set several attempts to wrong passwords to set locking the account to safeguard from the hackers.

5. Do not use Public WI-FI Networks

Are you try to keep an eye on your IoT devices through your mobile devices in different locations. I recommend you not to use the public WI-FI network to access them. Because they are easily accessible through for everyone, you are still in a hurry to access, use VPN that gives them protection against cyber-attacks, giving them privacy and security features, for example, using Express VPN.

6. Establish firewalls to discover the vulnerabilities

There are software and firewalls like intrusion detection system/intrusion prevention system in the market. This will be useful to screen and analyze the wire traffic of a network. You can identify the security weakness by the firewall scanners within the network structure. Use these firewalls to get rid of unwanted security issues and vulnerabilities.

7. Reconfigure your device settings

Every smart device comes with the insecure default settings, and sometimes we are not able to change these default settings configurations. These conditions need to be assessed and need to reconfigure the default settings.

8. Authenticate the IoT applications

Nowadays, every smart app offers authentication to secure the accounts. There are many types of authentication methods like single-factor authentication, two-step authentication, and multi-factor authentication. Use any one of these to send a one time password (OTP) to verify the user who logs in the smart device to keep our accounts from falling into the wrong hands.

9. Update the device software up to date

Every smart device manufacturer releases updates to fix bugs in their software. These security patches help us to improve our protection of the device. Also, update the software on the smartphone, which we are used to monitoring the IoT devices to avoid vulnerabilities.

10. Track the smartphones and keep them safe

When we connect the smart home to the smartphone and control them via smartphone, you need to keep them safe. If you miss the phone almost, every personal information is at risk to the cybercriminals. But sometimes it happens by accident, makes sure that you can clear all the data remotely.

However, securing smart devices is essential in the world of data. There are still cybercriminals bypassing the securities. So make sure to do the safety measures to avoid our accounts falling out into the wrong hands. I hope these steps will help you all to secure your IoT devices.

If you have any, feel free to share them in the comments! I’d love to know them.

Are you looking for more? Subscribe to weekly newsletters that can help your stay updated IoT application developments.

#iot #enterprise iot security #how iot can be used to enhance security #how to improve iot security #how to protect iot devices from hackers #how to secure iot devices #iot security #iot security devices #iot security offerings #iot security technologies iot security plus #iot vulnerable devices #risk based iot security program

Unsecured Microsoft Bing Server Leaked Search Queries, Location Data

An unsecured database has exposed sensitive data for users of Microsoft’s Bing search engine mobile application – including their location coordinates, search terms in clear text and more.

While no personal information, like names, were exposed, researchers with Wizcase argued that enough data was available that it would be possible to link these search queries and locations to user identities — giving bad actors information ripe for blackmail attacks, phishing scams and more.

The data was related to the mobile-app version of Microsoft Bing, housed in a 6.5 terabyte (TB) server owned by Microsoft. Researchers believe the server was password-protected until Sept. 10, two days before they uncovered the issue on Sept. 12. Microsoft was alerted to the exposed data on Sept. 13, and secured the server on Sept. 16.

#hacks #web security #cyber blackmail #cybercriminals #data exposed #exposed server #hack #meow attack #microsoft #microsoft bing #microsoft security #misconfiguration #phishing scams #search queries #security hack #unsecured server

Aisu  Joesph

Aisu Joesph

1624327316

Securing Microsoft Active Directory

Clustering

K-means is one of the simplest unsupervised machine learning algorithms that solve the well-known data clustering problem. Clustering is one of the most common data analysis tasks used to get an intuition about data structure. It is defined as finding the subgroups in the data such that each data points in different clusters are very different. We are trying to find the homogeneous subgroups within the data. Each group’s data points are similarly based on similarity metrics like a Euclidean-based distance or correlation-based distance.

The algorithm can do clustering analysis based on features or samples. We try to find the subcategory of sampling based on attributes or try to find the subcategory of parts based on samples. The practical applications of such a procedure are many: the best use of clustering in amazon and Netflix recommended system, given a medical image of a group of cells, a clustering algorithm could aid in identifying the centers of the cells; looking at the GPS data of a user’s mobile device, their more frequently visited locations within a certain radius can be revealed; for any set of unlabeled observations, clustering helps establish the existence of some structure of data that might indicate that the data is separable.

What is K-Means Clustering?

K-means the clustering algorithm whose primary goal is to group similar elements or data points into a cluster.

K in k-means represents the number of clusters.

A cluster refers to a collection of data points aggregated together because of certain similarities.

K-means clustering is an iterative algorithm that starts with k random numbers used as mean values to define clusters. Data points belong to the group represented by the mean value to which they are closest. This mean value co-ordinates called the centroid.

Iteratively, the mean value of each cluster’s data points is computed, and the new mean values are used to restart the process till the mean stops changing. The disadvantage of k-means is that it a local search procedure and could miss global patterns.

The k initial centroids can be randomly selected. Another approach of determining k is to compute the entire dataset’s mean and add _k _random co-ordinates to it to make k initial points. Another method is to determine the principal component of the data and divide it into _k _equal partitions. The mean of each section can be used as initial centroids.

#ad #microsoft #microsoft-azure #azure #azure-functions #azure-security

Christa  Stehr

Christa Stehr

1602964260

50+ Useful Kubernetes Tools for 2020 - Part 2

Introduction

Last year, we provided a list of Kubernetes tools that proved so popular we have decided to curate another list of some useful additions for working with the platform—among which are many tools that we personally use here at Caylent. Check out the original tools list here in case you missed it.

According to a recent survey done by Stackrox, the dominance Kubernetes enjoys in the market continues to be reinforced, with 86% of respondents using it for container orchestration.

(State of Kubernetes and Container Security, 2020)

And as you can see below, more and more companies are jumping into containerization for their apps. If you’re among them, here are some tools to aid you going forward as Kubernetes continues its rapid growth.

(State of Kubernetes and Container Security, 2020)

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